Training a model of a non-linear process

US8019701B2 · kind B2 · utility

Assignee

• Rockwell Automation Technologies, Inc. · US

Inventors

• Bijan Sayyar-Rodsari · Austin, US
• Edward Plumer · Georgetown, US
• Eric Hartman · Austin, US
• Kadir Liano · Pflugerville, US
• Celso Axelrud · Round Rock, US

Key dates

• Filing date: Apr 30, 2008
• Grant date: Sep 13, 2011
• Expiry date: Feb 15, 2029

Classification

• Technology area (CPC G): Physics
• CPC primary: G05B17/02
• WIPO field: Control
• WIPO sector: Instruments

Abstract

System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints.